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Non-coding RNAs (ncRNAs) are transcripts that do not code for proteins. Recent findings have shown that RNA-mediated regulatory mechanisms influence a substantial portion of typical microbial genomes. We present an efficient method for finding potential ncRNAs in bacteria by clustering genomic sequences based on homology inferred from both primary sequence and secondary structure. We evaluate our approach using a set of predominantly Firmicutes sequences. Our results showed that, though primary sequence based–homology search was inaccurate for diverged ncRNA sequences, through our clustering method, we were able to infer motifs that recovered nearly all members of most known ncRNA families. Hence, our method shows promise for discovering new families of ncRNA.

Noncoding RNAs (ncRNAs) have important functional roles in biological processes and have become a central research interest in modern molecular biology. However, how to find ncRNA attracts much more attention since ncRNA gene sequences do not have strong statistical signals, unlike protein coding genes. QRNA is a powerful program and has been widely used as an efficient analysis tool to detect ncRNA gene at present. Unfortunately, the O(L³) computing requirements and complicated data dependency greatly limit the usefulness of QRNA package with the explosion in gene database. In this paper, we present a fine-grained parallel QRNA prototype system, FPQRNA, for accelerating ncRNA gene detection application on FPGA chip. We propose a systolic-like array architecture with multiple PEs (Processing Elements). We partition the tasks by columns and assign tasks to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrices from external memory. The experimental results show a speedup factor of more than 18× over the QRNA - 2.0.3c software running on a PC platform with AMD Phenom 9650 Quad CPU for pairwise sequence alignment with 996 residues, however the power consumption of our FPGA accelerator is only about 30% of that of the general-purpose microprocessors.

Secondary structure remains the most exploitable feature for noncoding RNA (ncRNA) gene finding in genomes. However, methods based on secondary structure prediction may generate superfluous amount of candidates for validation and have yet to deliver the desired performance that can complement experimental efforts in ncRNA gene finding. This paper investigates a novel method, unpaired structural entropy (USE) as a measurement for the structure fold stability of ncRNAs. USE proves to be effective in identifying from the genome background a class of ncRNAs, such as precursor microRNAs (pre-miRNAs) that contains a long stem hairpin loop. USE correlates well and performs better than other measures on pre-miRNAs, including the previously formulated structural entropy. As an SVM classifier, USE outperforms existing pre-miRNA classifiers. A long stem hairpin loop is common for a number of other functional RNAs including introns splicing hairpins loops and intrinsic termination hairpin loops. We believe USE can be further applied in developing ab initio prediction programs for a larger class of ncRNAs.

The statistics about the open reading frames, the base compositions and the properties of the predicted secondary structures have potential to address the problem of discriminating coding and noncoding transcripts. Again, the Next Generation Sequencing platform, RNA-seq, provides us bounty of data from which expression profiles of the transcripts can be extracted which urged us adding a new set of dimension in this classification task. In this paper, we proposed CNCTDiscriminator — a coding and noncoding transcript discriminating system where we applied the integration of these four categories of features about the transcripts. The feature integration was done using both hypothesis learning and feature specific ensemble learning approaches. The CNCTDiscriminator model which was trained with composition and ORF features outperforms (precision 83.86%, recall 82.01%) other three popular methods — CPC (precision 98.31%, recall 25.95%), CPAT (precision 97.74%, recall 52.50%) and PORTRAIT (precision 84.37%, recall 73.2%) when applied to an independent benchmark dataset. However, the CNCTDiscriminator model that was trained using the ensemble approach shows comparable performance (precision 89.85%, recall 71.08%).

microRNAs (miRNAs) are short, noncoding RNAs that negatively regulate the levels of mRNA post-transcriptionally. Recent experiments revealed thousands of mRNA–miRNA pairs in which multiple miRNAs may bind the same transcript. These results raised the notion of miRNAs teamwork for a wide range of cellular context. miRror2.0 utilizes the miRNA-target predictions from over a dozen programs and resources and unifies them under a common statistical basis. The platform, called miRror2.0, considers the combinatorial regulation by miRNAs in different tissues, cell lines and under a broad range of conditions. A flexible setting permits the selection of the preferred combination of miRNA-target prediction resources as well as the statistical parameters for the analysis. miRror2.0 covers six major model organisms including human and mouse. Importantly, the system is capable of analyzing hundreds of genes that were subjected to miRNAs' regulation. Activating miRror2.0 by introducing thousands of genes from miRNA overexpression experiments successfully identified the objective miRNAs. The output from miRror2.0 is a list of genes that is optimally regulated by a defined set of miRNAs. A symmetric application of miRror2.0 starts with a set of miRNAs, and the system then seeks the preferred set of genes that are regulated by that miRNA composition. The results from miRror2.0 are empowered by an iterative procedure called PSI-miRror. PSI-miRror tests the robustness of miRror2.0 prediction. It allows a refinement of the initial list of genes in view of the miRNAs that optimally regulate this list. We present miRror2.0 as a valuable resource for supporting cellular experimentalists that seek recovery of combinatorial regulation by miRNAs from noisy experimental data. miRror2.0 is available at http://www.mirrorsuite.cs.huji.ac.il.

Four properties define exosomes. First, they are tiny bodies–as small as 35 nm in diameter, 1,000 times less than the width of a human hair—that perform key assignments in cell signaling and other biological processes. Second, their size aids in transiting hard-to-navigate tissue boundaries in the body, such as the brain–blood barrier and the gut–blood barrier, optioning an oral administration of therapy in some instances. Third, since they can convey protein peptides, nucleic acids, and small molecule drugs, they represent an amalgam of proteomic, genomic, and lipidomic concepts in biomedicine. And fourth, it is conceivable, exosomes can address any human disease—many of which cannot be accessed today—even using material from other species. Two research groups—in St. Louis and Montreal—first characterized them almost simultaneously in 1984, offering an explanation of how immature red blood cells lost their iron-transporting transferrin receptor when they matured. Their role as intercellular communicators grew in 1996 when researchers at the University of Utrecht showed how exosomes induced a powerful immune response that caused cancerous tumors to regress. They travel in every body fluid: blood, lymph, urine, tears, saliva, cerebrospinal, and mother’s milk. Originally seen in electron micrographs and thought to be inconsequential, they now have a presence in biotechnology as a new platform for diagnostics and therapy, broadly representing proteome medicine. As yet, they have not reached a critical mass for clinical adoption, though their prospects are tantalizing. This piece ends with a prediction—that by 2034, the 50th anniversary of the term exosome, proteome medicine will have several generally recognized as safe and effective exosome-based prescriptions, with China leading the way.

Melanoma is the most aggressive and deadly type of skin cancer and presents a major clinical challenge due to its ability to rapidly metastasize and become resistant to immune and targeted therapies. The identification and characterization of new molecular targets and pathways involved in the initiation, progression, and maintenance of melanoma will be critical for the development of superior treatments. Long non-coding RNAs (lncRNAs), a class of non-coding RNAs involved in regulating numerous cellular processes including tumor progression, cancer cell metastasis, and resistance to anti-cancer therapies, may be viable therapeutic targets in melanoma. In this review, we describe lncRNAs that contribute to melanoma development through microRNA (miRNA) sponging, regulation of metabolism, modification of the epigenome, or modulation of pro-tumorigenic signaling pathways. While more work remains to be done to characterize lncRNAs in melanoma, gaining a better understanding of their functions promises to yield a wide range of possibilities to improve melanoma diagnosis, prognosis, and treatment.

Non-coding RNAs (ncRNAs) are transcripts that do not code for proteins. Recent findings have shown that RNA-mediated regulatory mechanisms influence a substantial portion of typical microbial genomes. We present an efficient method for finding potential ncRNAs in bacteria by clustering genomic sequences based on homology inferred from both primary sequence and secondary structure. We evaluate our approach using a set of Firmicutes sequences, and the results show promise for discovering new ncRNAs.

RNAz is a widely used software package for de novo detection of structured noncoding RNAs in comparative genomics data. Four years of experience have not only demonstrated the applicability of the approach, but also helped us to identify limitations of the current implementation. RNAz 2.0 provides significant improvements in two respects: (1) The accuracy is increased by the systematic use of dinucleotide models. (2) Technical limitations of the previous version, such as the inability to handle alignments with more than six sequences, are overcome by increased training data and the usage of an entropy measure to represent sequence similarities. RNAz 2.0 shows a significantly lower false discovery rate on a dinucleotide background model than the previous version. Separate models for structural alignments provide an additional way to increase the predictive power. RNAz is open source software and can be obtained free of charge at: http://www.tbi.univie.ac.at/~wash/RNAz/

Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders characterized by deficits in social communication and presence of restricted interests and/or repetitive behaviors. Because of its high degree of heritability, genetic studies, focused mainly on the protein-coding regions of the genome, have attempted to identify gene mutations, both rare and common single nucleotide polymorphisms, and copy number variants that may cause or increase the susceptibility of this disorder. Nevertheless, no single gene, SNP, or DNA mutation can account for more than 1–2% of ASD cases. Recent studies have started to explore the genomic “dark matter” (DNA regions that do not directly code for proteins) which harbors many types of gene regulatory elements, including non-coding RNAs (ncRNA). However, ncRNA research in ASD is still in its infancy with only a few, albeit significant, ncRNA studies in the context of ASD. This article summarizes evidence that suggests future autism research needs to be expanded to cover the remaining 98% of the genome, especially ncRNAs, and emphasizes the need for subgrouping subjects to reduce the heterogeneity within study groups in order to draw comparisons between different independent cohorts.

An entirely new area of pharmacology is developing from an understanding of endogenous functional long noncoding RNAs (lncRNAs). Potential treatment options for cancer, diabetes and HIV are derived from findings that specific lncRNAs, which regulate networks of genes, can be targeted to treat the disorders. Emerging evidence indicates that specific lncRNAs also contribute to autism spectrum disorder (ASD) risk. Here, we review this evidence and explore possible treatment advances for ASD based on inhibitory RNA-based therapies.